A sliding window from a Data Stream

Interface for DSO_Window. Represents the points in the sliding window as micro-clusters.

Usage

DSC_Window(horizon = 100, lambda = 0)

Arguments

horizon

the window length.

lambda

decay factor damped window model. lambda = 0 means no dampening.

Value

An object of class DSC_Window (subclass of DSC, DSC_R, DSC_Micro).

Details

If lambda is greater than 0 then the weight uses a damped window model (Zhu and Shasha, 2002). The weight for points in the window follows \(2^{-lambda*t}\) where \(t\) is the age of the point.

References

Zhu, Y. and Shasha, D. (2002). StatStream: Statistical Monitoring of Thousands of Data Streams in Real Time, International Conference of Very Large Data Bases (VLDB'02).

See also

Other DSC_Micro: DSC_BICO(), DSC_BIRCH(), DSC_DBSTREAM(), DSC_DStream(), DSC_Micro(), DSC_Sample(), DSC_evoStream()

Author

Michael Hahsler

Examples

stream <- DSD_Gaussians(k = 3, d = 2, noise = 0.05)

window <- DSC_Window(horizon = 100)
window
#> Sliding window 
#> Class: DSC_Window, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 0 

update(window, stream, 200)
window
#> Sliding window 
#> Class: DSC_Window, DSC_Micro, DSC_R, DSC 
#> Number of micro-clusters: 100 

# plot micro-clusters
plot(window, stream)


# animation for a window using a damped window model. The weight decays
# with a half-life of 25
if (FALSE) { # \dontrun{
window <- DSC_Window(horizon = 25, lambda = 1 / 25)
animate_cluster(window, stream, horizon = 1, n = 100, xlim = c(0, 1), ylim = c(0, 1))
} # }